System and method for providing diagnosis and monitoring of respiratory insufficiency for use in automated patient care

ABSTRACT

An automated system and method for diagnosing and monitoring respiratory insufficiency and outcomes thereof is described. A plurality of monitoring sets is retrieved from a database. Each of the monitoring sets include recorded measures relating to patient information recorded on a substantially continuous basis. A patient status change is determined by comparing at least one recorded measure from each of the monitoring sets to at least one other recorded measure. Both recorded measures relate to the same type of patient information. Each patient status change is tested against an indicator threshold corresponding to the same type of patient information as the recorded measures which were compared. The indicator threshold corresponds to a quantifiable physiological measure of a pathophysiology indicative of respiratory insufficiency.

CROSS-REFERENCE TO RELATED APPLICATION

[0001] This patent application is a continuation of U.S. patentapplication Ser. No. 09/442,125, filed Nov. 16, 1999, pending, thedisclosure of which is incorporated herein by reference.

FIELD OF THE INVENTION

[0002] The present invention relates in general to respiratoryinsufficiency diagnosis and analysis, and, in particular, to anautomated collection and analysis patient care system and method fordiagnosing and monitoring respiratory insufficiency and outcomes thereofthroughout disease onset, progression, regression, and status quo.

BACKGROUND OF THE INVENTION

[0003] Presently, respiratory insufficiency due to primary diseases ofthe lungs is one of the leading causes of acute and chronic illness inthe world. Clinically, respiratory insufficiency involves eitherdifficulty in ventilation or in oxygenation. The former is manifest byincreases in the arterial partial pressure of carbon dioxide and thelatter is manifest by decreases in arterial partial pressure of oxygen.For purposes of this invention, the term “respiratory insufficiency”will refer to ventilatory insufficiency and/or to problems inoxygenation due to diseases of the lung. Common causes of respiratoryinsufficiency include bronchitis, emphysema, pneumonia, pulmonaryemboli, congestive heart failure, tumor infiltration of the lung andabnormalities of the interstitium of the lungs that may be infectious inorigin, due to immunological abnormalities, or as a result of exposureto environmental pathogens. The effects of respiratory insufficiencyrange from cough to impairment during physical exertion to a completefailure of lung function and respiratory arrest at any level ofactivity. Clinical manifestations of respiratory insufficiency includerespiratory distress, such as shortness of breath and fatigue, cough,and reduced exercise capacity or tolerance.

[0004] Several factors make the early diagnosis and prevention ofrespiratory insufficiency, as well as the monitoring of the progressionof respiratory insufficiency, relatively difficult. First, the onset ofrespiratory insufficiency is generally subtle and erratic. Often, thesymptoms are ignored and the patient compensates by changing his or herdaily activities. This situation is especially true in chronic lungdisorders where the onset of symptoms can be very gradual. As a result,many respiratory insufficiency conditions or deteriorations inrespiratory insufficiency remain undiagnosed until more serious problemsarise seriously limiting the activities of daily living.

[0005] The susceptibility to suffer from respiratory insufficiencydepends upon the patient's age, sex, physical condition, and otherfactors, such as smoking history, occupation, diabetes, co-existingheart disease, immunodepression, the presence or absence of cancer,surgical history, kidney function, and extent of pre-existing lungdisease. No one factor is dispositive. Finally, annual or even monthlylung checkups, including chest X-rays or other lung tests, provide, atbest, a “snapshot” of patient wellness and the incremental and subtleclinicophysiological changes which portend the onset or progression ofrespiratory insufficiency often go unnoticed, even with regular healthcare. Documentation of subtle improvements following therapy that canguide and refine further evaluation and therapy can be equally elusive.

[0006] Nevertheless, taking advantage of frequently and regularlymeasured physiological measures, such as recorded manually by a patient,via an external monitoring or therapeutic device, or via implantabledevice technologies, can provide a degree of detection and preventionheretofore unknown. For instance, patients already suffering from someform of treatable heart disease often receive an implantable pulsegenerator (IPG), cardiovascular or heart failure monitor, therapeuticdevice, or similar external wearable device, with which rhythm andstructural problems of the heart can be monitored and treated. Thesetypes of devices, although usually originally intended for use intreating some type of cardiac problem, can contain sufficientphysiological data to allow accurate assessment of lung disorders. Suchdevices are useful for detecting physiological changes in patientconditions through the retrieval and analysis of telemetered signalsstored in an on-board, volatile memory. Typically, these devices canstore more than thirty minutes of per heartbeat and respiratory cycledata recorded on a per heartbeat, per respiration, binned average basis,or on a derived basis from, for example, atrial or ventricularelectrical activity, minute ventilation, patient activity score, cardiacoutput score, arterial or mixed venous oxygen score, cardiopulmonarypressure measures, and the like. However, the proper analysis ofretrieved telemetered signals requires detailed medical subspecialtyknowledge, particularly by pulmonologists and cardiologists.

[0007] Alternatively, these telemetered signals can be remotelycollected and analyzed using an automated patient care system. One suchsystem is described in a related, commonly owned U.S. Pat. No.6,312,378, issued Nov. 6, 2001, the disclosure of which is incorporatedherein by reference. A medical device adapted to be implanted in anindividual patient records telemetered signals that are then retrievedon a regular, periodic basis using an interrogator or similarinterfacing device. The telemetered signals are downloaded via aninternetwork onto a network server on a regular, e.g., daily, basis andstored as sets of collected measures in a database along with otherpatient care records. The information is then analyzed in an automatedfashion and feedback, which includes a patient status indicator, isprovided to the patient.

[0008] While such an automated system can serve as a valuable tool inproviding remote patient care, an approach to systematically correlatingand analyzing the raw collected telemetered signals, as well as manuallycollected physiological measures, through applied pulmonary andcardiovascular medical knowledge to accurately diagnose the onset of aparticular medical condition, such as respiratory insufficiency, isneeded, especially in patients with co-existing heart disease. Oneautomated patient care system directed to a patient-specific monitoringfunction is described in U.S. Pat. No. 5,113,869 ('869) to Nappholz etal. The '869 patent discloses an implantable, programmableelectrocardiography (ECG) patient monitoring device that senses andanalyzes ECG signals to detect ECG and physiological signalcharacteristics predictive of malignant cardiac arrhythmias. Themonitoring device can communicate a warning signal to an external devicewhen arrhythmias are predicted. However, the Nappholz device is limitedto detecting tachycardias. Unlike requirements for automated respiratoryinsufficiency monitoring, the Nappholz device focuses on rudimentary ECGsignals indicative of malignant cardiac tachycardias, an already wellestablished technique that can be readily used with on-board signaldetection techniques. Also, the Nappholz device is patient specific onlyand is unable to automatically take into consideration a broader patientor peer group history for reference to detect and consider theprogression or improvement of lung disease. Moreover, the Nappholzdevice has a limited capability to automatically self-reference multipledata points in time and cannot detect disease regression even in theindividual patient. Also, the Nappholz device must be implanted andcannot function as an external monitor. Finally, the Nappholz device isincapable of tracking the cardiovascular and cardiopulmonaryconsequences of any rhythm disorder.

[0009] Consequently, there is a need for a systematic approach todetecting trends in regularly collected physiological data indicative ofthe onset, progression, regression, or status quo of respiratoryinsufficiency diagnosed and monitored using an automated, remote patientcare system. The physiological data could be telemetered signals datarecorded either by an external or an implantable medical device or,alternatively, individual measures collected through manual means.Preferably, such an approach would be capable of diagnosing both acuteand chronic respiratory insufficiency conditions, as well as thesymptoms of other lung disorders. In addition, findings from individual,peer group, and general population patient care records could beintegrated into continuous, on-going monitoring and analysis.

SUMMARY OF THE INVENTION

[0010] The present invention provides a system and method for diagnosingand monitoring the onset, progression, regression, and status quo ofrespiratory insufficiency using an automated collection and analysispatient care system. Measures of patient cardiopulmonary information areeither recorded by an external or implantable medical device, such as anIPG, cardiovascular or heart failure monitor, or respiratory diagnosticor therapeutic device, or manually through conventional patient-operablemeans. The measures are collected on a regular, periodic basis forstorage in a database along with other patient care records. Derivedmeasures are developed from the stored measures. Select stored andderived measures are analyzed and changes in patient condition arelogged. The logged changes are compared to quantified indicatorthresholds to detect findings of respiratory distress or reducedexercise capacity indicative of the principal pathophysiologicalmanifestations of respiratory insufficiency: elevated partial pressureof arterial carbon dioxide and reduced partial pressure of arterialoxygen.

[0011] An embodiment of the present invention is an automated system andmethod for diagnosing and monitoring respiratory insufficiency andoutcomes thereof. A plurality of monitoring sets is retrieved from adatabase. Each of the monitoring sets include recorded measures relatingto patient information recorded on a substantially continuous basis. Apatient status change is determined by comparing at least one recordedmeasure from each of the monitoring sets to at least one other recordedmeasure. Both recorded measures relate to the same type of patientinformation. Each patient status change is tested against an indicatorthreshold corresponding to the same type of patient information as therecorded measures that were compared. The indicator thresholdcorresponds to a quantifiable physiological measure of a pathophysiologyindicative of respiratory insufficiency.

[0012] A further embodiment is an automated collection and analysispatient care system and method for diagnosing and monitoring respiratoryinsufficiency and outcomes thereof. A plurality of monitoring sets isretrieved from a database. Each monitoring set includes recordedmeasures that each relate to patient information and include eithermedical device measures or derived measures calculable therefrom. Themedical device measures are recorded on a substantially continuousbasis. A set of indicator thresholds is defined. Each indicatorthreshold corresponds to a quantifiable physiological measure of apathophysiology indicative of respiratory insufficiency and relates tothe same type of patient information as at least one of the recordedmeasures. A respiratory insufficiency finding is diagnosed. A change inpatient status is determined by comparing at least one recorded measureto at least one other recorded measure with both recorded measuresrelating to the same type of patient information. Each patient statuschange is compared to the indicator threshold corresponding to the sametype of patient information as the recorded measures that were compared.

[0013] A further embodiment is an automated patient care system andmethod for diagnosing and monitoring respiratory insufficiency andoutcomes thereof. Recorded measures organized into a monitoring set foran individual patient are stored into a database. Each recorded measureis recorded on a substantially continuous basis and relates to at leastone aspect of monitoring reduced exercise capacity and/or respiratorydistress. A plurality of the monitoring sets is periodically retrievedfrom the database. At least one measure related to respiratoryinsufficiency onset, progression, regression, and status quo isevaluated. A patient status change is determined by comparing at leastone recorded measure from each of the monitoring sets to at least oneother recorded measure with both recorded measures relating to the sametype of patient information. Each patient status change is testedagainst an indicator threshold corresponding to the same type of patientinformation as the recorded measures that were compared. The indicatorthreshold corresponds to a quantifiable physiological measure of apathophysiology indicative of reduced exercise capacity and/orrespiratory distress.

[0014] The present invention provides a capability to detect and tracksubtle trends and incremental changes in recorded patientcardiopulmonary information for diagnosing and monitoring respiratoryinsufficiency. When coupled with an enrollment in a remote patientmonitoring service having the capability to remotely and continuouslycollect and analyze external or implantable medical device measures,respiratory insufficiency detection, prevention and tracking regressionfrom therapeutic maneuvers become feasible.

[0015] Still other embodiments of the present invention will becomereadily apparent to those skilled in the art from the following detaileddescription, wherein is described embodiments of the invention by way ofillustrating the best mode contemplated for carrying out the invention.As will be realized, the invention is capable of other and differentembodiments and its several details are capable of modifications invarious obvious respects, all without departing from the spirit and thescope of the present invention. Accordingly, the drawings and detaileddescription are to be regarded as illustrative in nature and not asrestrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

[0016]FIG. 1 is a block diagram showing an automated collection andanalysis patient care system for diagnosing and monitoring respiratoryinsufficiency and outcomes thereof in accordance with the presentinvention;

[0017]FIG. 2 is a database schema showing, by way of example, theorganization of a device and derived measures set record for care ofpatients with respiratory insufficiency stored as part of a patient carerecord in the database of the system of FIG. 1;

[0018]FIG. 3 is a database schema showing, by way of example, theorganization of a quality of life and symptom measures set record forcare of patients with respiratory insufficiency stored as part of apatient care record in the database of the system of FIG. 1;

[0019]FIG. 4 is a database schema showing, by way of example, theorganization of a combined measures set record for care of patients withrespiratory insufficiency stored as part of a patient care record in thedatabase of the system of FIG. 1;

[0020]FIG. 5 is a block diagram showing the software modules of theserver system of the system of FIG. 1;

[0021]FIG. 6 is a record view showing, by way of example, a set ofpartial patient care records for care of patients with respiratoryinsufficiency stored in the database of the system of FIG. 1;

[0022]FIG. 7 is a Venn diagram showing, by way of example, peer groupoverlap between the partial patient care records of FIG. 6;

[0023] FIGS. 8A-8B are flow diagrams showing a method for diagnosing andmonitoring respiratory insufficiency and outcomes thereof using anautomated collection and analysis patient care system in accordance withthe present invention;

[0024]FIG. 9 is a flow diagram showing the routine for retrievingreference baseline sets for use in the method of FIGS. 8A-8B;

[0025]FIG. 10 is a flow diagram showing the routine for retrievingmonitoring sets for use in the method of FIGS. 8A-8B;

[0026] FIGS. 11A-11F are flow diagrams showing the routine for testingthreshold limits for use in the method of FIGS. 8A-8B;

[0027]FIG. 12 is a flow diagram showing the routine for evaluating theonset, progression, regression, and status quo of respiratoryinsufficiency for use in the method of FIGS. 8A-8B;

[0028] FIGS. 13A-13C are flow diagrams showing the routine fordetermining an onset of respiratory insufficiency for use in the routineof FIG. 12;

[0029] FIGS. 14A-14C are flow diagrams showing the routine fordetermining progression or worsening of respiratory insufficiency foruse in the routine of FIG. 12;

[0030] FIGS. 15A-15C are flow diagrams showing the routine fordetermining regression or improving of respiratory insufficiency for usein the routine of FIG. 12; and

[0031]FIG. 16 is a flow diagram showing the routine for determiningthreshold stickiness (“hysteresis”) for use in the method of FIG. 12.

DETAILED DESCRIPTION

[0032]FIG. 1 is a block diagram showing an automated collection andanalysis patient care system 10 for diagnosing and monitoringrespiratory insufficiency in accordance with the present invention. Anexemplary automated collection and analysis patient care system suitablefor use with the present invention is disclosed in the related,commonly-owned U.S. Pat. No. 6,312,378, issued Nov. 6, 2001, thedisclosure of which is incorporated herein by reference. Preferably, anindividual patient 11 is a recipient of an implantable medical device12, such as, by way of example, an IPG, cardiovascular, heart failuremonitor, pulmonary monitor, or therapeutic device, with a set of leadsextending into his or her heart and electrodes implanted throughout thecardiopulmonary system. Alternatively, an external monitoring ortherapeutic medical device 26, a subcutaneous monitor or device insertedinto other organs, a cutaneous monitor, or even a manual physiologicalmeasurement device, such as an respiratory monitor, electrocardiogram orheart rate monitor, could be used. The implantable medical device 12 andexternal medical device 26 include circuitry for recording into ashort-term, volatile memory telemetered signals stored for laterretrieval, which become part of a set of device and derived measures,such as described below, by way of example, with reference to FIG. 2.Exemplary implantable medical devices suitable for use in the presentinvention include the Discovery line of pacemakers, manufactured byGuidant Corporation, Indianapolis, Ind., and the Gem line of ICDs,manufactured by Medtronic Corporation, Minneapolis, Minn.

[0033] The telemetered signals stored in the implantable medical device12 are preferably retrieved upon the completion of an initialobservation period and subsequently thereafter on a continuous, periodic(daily) basis, such as described in the related, commonly-owned U.S.Pat. No. 6,221,011, issued Apr. 24, 2001, the disclosure of which isincorporated herein by reference. A programmer 14, personal computer 18,or similar device for communicating with an implantable medical device12 can be used to retrieve the telemetered signals. A magnetized reedswitch (not shown) within the implantable medical device 12 closes inresponse to the placement of a wand 13 over the site of the implantablemedical device 12. The programmer 14 sends programming or interrogatinginstructions to and retrieves stored telemetered signals from theimplantable medical device 12 via RF signals exchanged through the wand13. Similar communication means are used for accessing the externalmedical device 26. Once downloaded, the telemetered signals are sent viaan internetwork 15, such as the Internet, to a server system 16 whichperiodically receives and stores the telemetered signals as devicemeasures in patient care records 23 in a database 17, as furtherdescribed below, by way of example, with reference to FIGS. 2 and 3. Anexemplary programmer 14 suitable for use in the present invention is theModel 2901 Programmer Recorder Monitor, manufactured by GuidantCorporation, Indianapolis, Ind.

[0034] The patient 11 is remotely monitored by the server system 16 viathe internetwork 15 through the periodic receipt of the retrieved devicemeasures from the implantable medical device 12 or external medicaldevice 26. The patient care records 23 in the database 17 are organizedinto two identified sets of device measures: an optional referencebaseline 26 recorded during an initial observation period and monitoringsets 27 recorded subsequently thereafter. The device measures sets areperiodically analyzed and compared by the server system 16 to indicatorthresholds corresponding to quantifiable physiological measures of apathophysiology indicative of respiratory insufficiency, as furtherdescribed below with reference to FIG. 5. As necessary, feedback isprovided to the patient 11. By way of example, the feedback includes anelectronic mail message automatically sent by the server system 16 overthe internetwork 15 to a personal computer 18 (PC) situated for localaccess by the patient 11. Alternatively, the feedback can be sentthrough a telephone interface device 19 as an automated voice mailmessage to a telephone 21 or as an automated facsimile message to afacsimile machine 22, both also situated for local access by the patient11. Moreover, simultaneous notifications can also be delivered to thepatient's physician, hospital, or emergency medical services provider 29using similar feedback means to deliver the information.

[0035] The server system 10 can consist of either a single computersystem or a cooperatively networked or clustered set of computersystems. Each computer system is a general purpose, programmed digitalcomputing device consisting of a central processing unit (CPU), randomaccess memory (RAM), non-volatile secondary storage, such as a harddrive or CD ROM drive, network interfaces, and peripheral devices,including user interfacing means, such as a keyboard and display.Program code, including software programs, and data are loaded into theRAM for execution and processing by the CPU and results are generatedfor display, output, transmittal, or storage, as is known in the art.

[0036] The database 17 stores patient care records 23 for eachindividual patient to whom remote patient care is being provided. Eachpatient care record 23 contains normal patient identification andtreatment profile information, as well as medical history, medicationstaken, height and weight, and other pertinent data (not shown). Thepatient care records 23 consist primarily of two sets of data: deviceand derived measures (D&DM) sets 24 a, 24 b and quality of life (QOL)sets 25 a, 25 b, the organization of which are further described belowwith respect to FIGS. 2 and 3, respectively. The device and derivedmeasures sets 24 a, 24 b and quality of life and symptom measures sets25 a, 25 b can be further logically categorized into two potentiallyoverlapping sets. The reference baseline 26 is a special set of deviceand derived reference measures sets 24 a and quality of life and symptommeasures sets 25 a recorded and determined during an initial observationperiod. Monitoring sets 27 are device and derived measures sets 24 b andquality of life and symptom measures sets 25 b recorded and determinedthereafter on a regular, continuous basis. Other forms of databaseorganization are feasible.

[0037] The implantable medical device 12 and, in a more limited fashion,the external medical device 26, record patient information for care ofpatients with respiratory insufficiency on a regular basis. The recordedpatient information is downloaded and stored in the database 17 as partof a patient care record 23. Further patient information can be derivedfrom recorded data, as is known in the art. FIG. 2 is a database schemashowing, by way of example, the organization of a device and derivedmeasures set record 40 for patient care stored as part of a patient carerecord in the database 17 of the system of FIG. 1. Each record 40 storespatient information which includes a snapshot of telemetered signalsdata which were recorded by the implantable medical device 12 or theexternal medical device 26, for instance, on per heartbeat, binnedaverage or derived bases; measures derived from the recorded devicemeasures; and manually collected information, such as obtained through apatient medical history interview or questionnaire. The followingnon-exclusive information can be recorded for a patient: atrialelectrical activity 41, ventricular electrical activity 42, PR intervalor AV interval 43, QRS measures 44, ST-T wave measures 45, QT interval46, body temperature 47, patient activity score 48, posture 49,cardiovascular pressures 50, pulmonary artery systolic pressure measure51, pulmonary artery diastolic pressure measure 52, respiratory rate 53,ventilatory tidal volume 54, minute ventilation 55, transthoracicimpedance 56, cardiac output 57, systemic blood pressure 58, patientgeographic location (altitude) 59, mixed venous oxygen score 60,arterial oxygen score 61, arterial carbon dioxide score 62, acidity (pH)level 63, potassium [K+] level 64, sodium [Na+] level 65, glucose level66, blood urea nitrogen (BUN) and creatinine 67, hematocrit 68, hormonallevels 69, lung injury chemical tests 70, cardiac injury chemical tests71, myocardial blood flow 72, central nervous system (CNS) injurychemical tests 73, central nervous system blood flow 74, interventionsmade by the implantable medical device or external medical device 75,and the relative success of any interventions made 76. In addition, theimplantable medical device or external medical device communicatesdevice-specific information, including battery status, general devicestatus and program settings 77 and the time of day 78 for the variousrecorded measures. Other types of collected, recorded, combined, orderived measures are possible, as is known in the art.

[0038] The device and derived measures sets 24 a, 24 b (shown in FIG.1), along with quality of life and symptom measures sets 25 a, 25 b, asfurther described below with reference to FIG. 3, are continuously andperiodically received by the server system 16 as part of the on-goingpatient care monitoring and analysis function. These regularly collecteddata sets are collectively categorized as the monitoring sets 27 (shownin FIG. 1). In addition, select device and derived measures sets 24 aand quality of life and symptom measures sets 25 a can be designated asa reference baseline 26 at the outset of patient care to improve theaccuracy and meaningfulness of the serial monitoring sets 27. Selectpatient information is collected, recorded, and derived during aninitial period of observation or patient care, such as described in therelated, commonly-owned U.S. Pat. No. 6,221,011, issued Apr. 24, 2001,the disclosure of which is incorporated herein by reference.

[0039] As an adjunct to remote patient care through the monitoring ofmeasured physiological data via the implantable medical device 12 orexternal medical device 26, quality of life and symptom measures sets 25a can also be stored in the database 17 as part of the referencebaseline 26, if used, and the monitoring sets 27. A quality of lifemeasure is a semi-quantitative self-assessment of an individualpatient's physical and emotional well being and a record of symptoms,such as provided by the Duke Activities Status Indicator. These scoringsystems can be provided for use by the patient 11 on the personalcomputer 18 (shown in FIG. 1) to record his or her quality of lifescores for both initial and periodic download to the server system 16.FIG. 3 is a database schema showing, by way of example, the organizationof a quality of life record 80 for use in the database 17. The followinginformation is recorded for a patient: overall health wellness 81,psychological state 82, activities of daily living 83, work status 84,geographic location 85, family status 86, shortness of breath 87, cough88, sputum production 89, sputum color 90, energy level 91, exercisetolerance 92, chest discomfort 93, and time of day 94, and other qualityof life and symptom measures as would be known to one skilled in theart.

[0040] The patient may also add non-device quantitative measures, suchas the six-minute walk distance, as complementary data to the device andderived measures sets 24 a, 24 b and the symptoms during the six-minutewalk to quality of life and symptom measures sets 25 a, 25 b.

[0041] Other types of quality of life and symptom measures are possible,such as those indicated by responses to the Minnesota Living with HeartFailure Questionnaire described in E. Braunwald, ed., “Heart Disease—ATextbook of Cardiovascular Medicine,” pp. 452-454, W. B. Saunders Co. (1997 ), the disclosure of which is incorporated herein by reference.Similarly, functional classifications based on the relationship betweensymptoms and the amount of effort required to provoke them can serve asquality of life and symptom measures, such as the New York HeartAssociation (NYHA) classifications I, II, III and IV, adapted for usefor lung disease rather than heart disease, also described in Ibid.

[0042] On a periodic basis, the patient information stored in thedatabase 17 is analyzed and compared to pre-determined cutoff levels,which, when exceeded, can provide etiological indications of respiratoryinsufficiency symptoms. FIG. 4 is a database schema showing, by way ofexample, the organization of a combined measures set record 95 for usein the database 17. Each record 95 stores patient information obtainedor derived from the device and derived measures sets 24 a, 24 b andquality of life and symptom measures sets 25 a, 25 b as maintained inthe reference baseline 26, if used, and the monitoring sets 27. Thecombined measures set 95 represents those measures most (but notexhaustively or exclusively) relevant to a pathophysiology indicative ofrespiratory insufficiency and are determined as further described belowwith reference to FIGS. 8A-8B. The following information is stored for apatient: heart rate 96, heart rhythm (e.g., normal sinus vs. atrialfibrillation) 97, pacing modality 98, pulmonary artery systolic pressuremeasure 99, pulmonary artery diastolic pressure measure 100, cardiacoutput score 101, arterial oxygen score 102, mixed venous oxygen score103, respiratory rate 104, tidal volume 105, transthoracic impedance106, arterial carbon dioxide score 107, right ventricular peak systolicpressure 108, pulmonary artery end diastolic pressure 109, patientactivity score 110, posture 111, exercise tolerance quality of life andsymptom measures 112, respiratory distress quality of life and symptommeasures 113, cough 114, sputum production 115, any interventions madeto treat respiratory insufficiency 116, including treatment by medicaldevice, via drug infusion administered by the patient or by a medicaldevice, surgery, and any other form of medical intervention as is knownin the art, the relative success of any such interventions made 117, andtime of day 118. Other types of comparison measures regardingrespiratory insufficiency are possible as is known in the art. In thedescribed embodiment, each combined measures set 95 is sequentiallyretrieved from the database 17 and processed. Alternatively, eachcombined measures set 95 could be stored within a dynamic data structuremaintained transitorily in the random access memory of the server system16 during the analysis and comparison operations.

[0043]FIG. 5 is a block diagram showing the software modules of theserver system 16 of the system 10 of FIG. 1. Each module is a computerprogram written as source code in a conventional programming language,such as the C or Java programming languages, and is presented forexecution by the CPU of the server system 16 as object or byte code, asis known in the art. The various implementations of the source code andobject and byte codes can be held on a computer-readable storage mediumor embodied on a transmission medium in a carrier wave. The serversystem 16 includes three primary software modules, database module 125,diagnostic module 126, and feedback module 128, which perform integratedfunctions as follows.

[0044] First, the database module 125 organizes the individual patientcare records 23 stored in the database 17 (shown in FIG. 1) andefficiently stores and accesses the reference baseline 26, monitoringsets 27, and patient care data maintained in those records. Any type ofdatabase organization could be utilized, including a flat file system,hierarchical database, relational database, or distributed database,such as provided by database vendors, such as Oracle Corporation,Redwood Shores, Calif.

[0045] Next, the diagnostic module 126 makes findings of respiratoryinsufficiency based on the comparison and analysis of the data measuresfrom the reference baseline 26 and monitoring sets 27. The diagnosticmodule includes three modules: comparison module 130, analysis module131, and quality of life module 132. The comparison module 130 comparesrecorded and derived measures retrieved from the reference baseline 26,if used, and monitoring sets 27 to indicator thresholds 129. Thedatabase 17 stores individual patient care records 23 for patientssuffering from various health disorders and diseases for which they arereceiving remote patient care. For purposes of comparison and analysisby the comparison module 130, these records can be categorized into peergroups containing the records for those patients suffering from similardisorders, as well as being viewed in reference to the overall patientpopulation. The definition of the peer group can be progressivelyrefined as the overall patient population grows. To illustrate, FIG. 6is a record view showing, by way of example, a set of partial patientcare records for care of patients with respiratory insufficiency storedin the database 17 for three patients, Patient 1, Patient 2, and Patient3. For each patient, three sets of peer measures, X, Y, and Z, areshown. Each of the measures, X, Y, and Z, could be either collected orderived measures from the reference baseline 26, if used, and monitoringsets 27.

[0046] The same measures are organized into time-based sets with Set 0representing sibling measures made at a reference time t=0. Similarly,Set n−2, Set n−1 and Set n each represent sibling measures made at laterreference times t=n−2, t=n−1 and t=n, respectively. Thus, for a givenpatient, such as Patient 1, serial peer measures, such as peer measureX₀through X_(n), represent the same type of patient informationmonitored over time. The combined peer measures for all patients can becategorized into a health disorder- or disease-matched peer group. Thedefinition of disease-matched peer group is a progressive definition,refined over time as the number of monitored patients grows. Measuresrepresenting different types of patient information, such as measuresX₀, Y₀, and Z₀, are sibling measures. These are measures which are alsomeasured over time, but which might have medically significant meaningwhen compared to each other within a set for an individual patient.

[0047] The comparison module 130 performs two basic forms ofcomparisons. First, individual measures for a given patient can becompared to other individual measures for that same patient(self-referencing). These comparisons might be peer-to-peer measures,that is, measures relating to a one specific type of patientinformation, projected over time, for instance, X_(n), X_(n−1), X_(n−2),. . . X₀, or sibling-to-sibling measures, that is, measures relating tomultiple types of patient information measured during the same timeperiod, for a single snapshot, for instance, X_(n), Y_(n), and Z_(n), orprojected over time, for instance, X_(n), Y_(n), Z_(n), X_(n−1),Y_(n−1), Z_(n−1), X_(n−2), Y_(n−2), Z_(n−2), . . . X₀, Y₀, Z₀. Second,individual measures for a given patient can be compared to otherindividual measures for a group of other patients sharing the samedisorder- or disease-specific characteristics (peer group referencing)or to the patient population in general (population referencing). Again,these comparisons might be peer-to-peer measures projected over time,for instance, X_(n), X_(n′), X_(n″), X_(n−1), X_(n−1′), X_(n−1″),X_(n−2), X_(n−2′), X_(n−2″), . . . X₀, X_(0′),X_(0″), or comparing theindividual patient's measures to an average from the group. Similarly,these comparisons might be sibling-to-sibling measures for singlesnapshots, for instance, X_(n), X_(n′), X_(n″), Y_(n), Y_(n′), Y_(n″),and Z_(n), Z_(n′), Z_(n″), or projected over time, for instance, X_(n),X_(n′), X_(n″), Y_(n), Y_(n′), Y_(n″), Z_(n), Z_(n′), Z_(n″), X_(n−1),X_(n−1′), X_(n−1″), Y_(n−1), Y_(n−1′), Y_(n−1″), Z_(n−1), Z_(n−l′),Z_(n−1″), X_(n−2), X_(n−2′), X_(n−2″), Y_(n −2), Y_(n−2′), Y_(n−2″),Z_(n−2), Z_(n−2′), Z_(n−2″) . . . X₀, X_(0′), X_(0″), Y₀, Y_(0′),Y_(0″), and Z₀, Z_(0′), Z_(0″). Other forms of comparisons are feasible,including multiple disease diagnoses for diseases exhibiting similarabnormalities in physiological measures that result from a seconddisease but manifest in different combinations or onset in differenttemporal sequences.

[0048]FIG. 7 is a Venn diagram showing, by way of example, peer groupoverlap between the partial patient care records 23 of FIG. 1. Eachpatient care record 23 includes characteristics data 350, 351, 352,including personal traits, demographics, medical history, and relatedpersonal data, for patients 1, 2 and 3, respectively. For example, thecharacteristics data 350 for patient 1 might include personal traitswhich include gender and age, such as male and an age between 40-45; ademographic of resident of New York City; and a medical historyconsisting of chronic bronchitis, recurrent pneumonia, a history of aninferior myocardial infarction and diabetes. Similarly, thecharacteristics data 351 for patient 2 might include identical personaltraits, thereby resulting in partial overlap 353 of characteristics data350 and 351. Similar characteristics overlap 354, 355, 356 can existbetween each respective patient. The overall patient population 357would include the universe of all characteristics data. As themonitoring population grows, the number of patients with personal traitsmatching those of the monitored patient will grow, increasing the valueof peer group referencing. Large peer groups, well matched across allmonitored measures, will result in a well known natural history ofdisease and will allow for more accurate prediction of the clinicalcourse of the patient being monitored. If the population of patients isrelatively small, only some traits 356 will be uniformly present in anyparticular peer group. Eventually, peer groups, for instance, composedof 100 or more patients each, would evolve under conditions in whichthere would be complete overlap of substantially all salient data,thereby forming a powerful core reference group for any new patientbeing monitored.

[0049] Referring back to FIG. 5, the analysis module 131 analyzes theresults from the comparison module 130, which are stored as a combinedmeasures set 95 (not shown), to a set of indicator thresholds 129, asfurther described below with reference to FIGS. 8A-8B. Similarly, thequality of life module 132 compares quality of life and symptom measures25 a, 25 b from the reference baseline 26 and monitoring sets 27, theresults of which are incorporated into the comparisons performed by theanalysis module 131, in part, to either refute or support the findingsbased on physiological “hard” data. Finally, the feedback module 128provides automated feedback to the individual patient based, in part, onthe patient status indicator 127 generated by the diagnostic module 126.As described above, the feedback could be by electronic mail or byautomated voice mail or facsimile. The feedback can also includenormalized voice feedback, such as described in the related,commonly-owned U.S. Pat. No. 6,203,495, issued Mar. 20, 2001, thedisclosure of which is incorporated herein by reference. In addition,the feedback module 128 determines whether any changes to interventivemeasures are appropriate based on threshold stickiness (“hysteresis”)133, as further described below with reference to FIG. 16. The thresholdstickiness 133 can prevent fickleness in the diagnostic routinesresulting from transient, non-trending and non-significant fluctuationsin the various collected and derived measures in favor of more certaintyin diagnosis. However, in the case of some of the parameters beingfollowed, such as activity and pulmonary artery systolic and diastolicpressures, abrupt spikes in these measures can be indicative of coughingand therefore helpful in indicating the onset of pulmonaryinsufficiency. In a further embodiment of the present invention, thefeedback module 128 includes a patient query engine 134 that enables theindividual patient 11 to interactively query the server system 16regarding the diagnosis, therapeutic maneuvers, and treatment regimen.Conversely, the patient query engines 134, found in interactive expertsystems for diagnosing medical conditions, can interactively query thepatient. Using the personal computer 18 (shown in FIG. 1), the patientcan have an interactive dialogue with the automated server system 16, aswell as human experts as necessary, to self assess his or her medicalcondition. Such expert systems are well known in the art, an example ofwhich is the MYCIN expert system developed at Stanford University anddescribed in Buchanan, B. & Shortlife, E., “RULE-BASED EXPERT SYSTEMS.The MYCIN Experiments of the Stanford Heuristic Programming Project,”Addison-Wesley (1984 ). The various forms of feedback described abovehelp to increase the accuracy and specificity of the reporting of thequality of life and symptomatic measures.

[0050] FIGS. 8A-8B are flow diagrams showing a method for diagnosing andmonitoring respiratory insufficiency and outcomes thereof 135 using anautomated collection and analysis patient care system 10 in accordancewith the present invention. First, the indicator thresholds 129 (shownin FIG. 5) are set (block 136 ) by defining a quantifiable physiologicalmeasure of a pathophysiology indicative of respiratory insufficiency andrelating to the each type of patient information in the combined deviceand derived measures set 95 (shown in FIG. 4). The actual values of eachindicator threshold can be finite cutoff values, weighted values, orstatistical ranges, as discussed below with reference to FIGS. 11A-11F.Next, the reference baseline 26 (block 137) and monitoring sets 27(block 138) are retrieved from the database 17, as further describedbelow with reference to FIGS. 9 and 10, respectively. Each measure inthe combined device and derived measures set 95 is tested against thethreshold limits defined for each indicator threshold 129 (block 139),as further described below with reference to FIGS. 11A-11F. Thepotential onset, progression, regression, or status quo of respiratoryinsufficiency is then evaluated (block 140) based upon the findings ofthe threshold limits tests (block 139), as further described below withreference to FIGS. 13A-13C, 14A-14C, 15A-15C.

[0051] In a further embodiment, multiple near-simultaneous disorders areconsidered in addition to primary respiratory insufficiency. Primaryrespiratory insufficiency is defined as the onset or progression ofrespiratory insufficiency without obvious inciting cause. Secondaryrespiratory insufficiency is defined as the onset or progression ofrespiratory insufficiency (in a patient with or without pre-existingrespiratory insufficiency) from another disease process, such ascongestive heart failure, coronary insufficiency, atrial fibrillation,and so forth. Other health disorders and diseases can potentially sharethe same forms of symptomatology as respiratory insufficiency, such ascongestive heart failure, myocardial ischemia, pneumonia, exacerbationof chronic bronchitis, renal failure, sleep-apnea, stroke, anemia,atrial fibrillation, other cardiac arrhythmias, and so forth. If morethan one abnormality is present, the relative sequence and magnitude ofonset of abnormalities in the monitored measures becomes most importantin sorting and prioritizing disease diagnosis and treatment.

[0052] Thus, if other disorders or diseases are being cross-referencedand diagnosed (block 141), their status is determined (block 142). Inthe described embodiment, the operations of ordering and prioritizingmultiple near-simultanous disorders (box 151) by the testing ofthreshold limits and analysis in a manner similar to congestive heartfailure as described above, preferably in parallel to the presentdetermination, is described in the related, commonly-owned U.S. patentapplication, Ser. No. 09/441,405, filed Nov. 16, 1999, pending, thedisclosure of which is incorporated herein by reference. If respiratoryinsufficiency is due to an obvious inciting cause, i.e., secondaryrespiratory insufficiency, (block 143 ), an appropriate treatmentregimen for respiratory insufficiency as exacerbated by other disordersis adopted that includes treatment of secondary disorders, e.g.,congestive heart failure, myocardial ischemia, atrial fibrillation, andso forth (block 144) and a suitable patient status indicator 127 forrespiratory insufficiency is provided (block 146) to the patient.Suitable devices and approaches to diagnosing and treating congestiveheart failure, myocardial infarction, and atrial fibrillation aredescribed in related, commonly-owned U.S. Pat. No. 6,336,903, issuedJan. 8, 2002; U.S. Pat. No. 6,368,284, issued Apr. 9, 2002; and U.S.patent application Ser. No. 09/441,613, filed Nov. 16, 1999, pending,the disclosures of which are incorporated herein by reference.

[0053] Otherwise, if primary respiratory insufficiency is indicated(block 143), a primary treatment regimen is followed (block 145). Apatient status indicator 127 for respiratory insufficiency is provided(block 146) to the patient regarding physical well-being, diseaseprognosis, including any determinations of disease onset, progression,regression, or status quo, and other pertinent medical and generalinformation of potential interest to the patient.

[0054] Finally, in a further embodiment, if the patient submits a queryto the server system 16 (block 147), the patient query is interactivelyprocessed by the patient query engine (block 148). Similarly, if theserver elects to query the patient (block 149), the server query isinteractively processed by the server query engine (block 150). Themethod then terminates if no further patient or server queries aresubmitted.

[0055]FIG. 9 is a flow diagram showing the routine for retrievingreference baseline sets 137 for use in the method of FIGS. 8A-8B. Thepurpose of this routine is to retrieve the appropriate referencebaseline sets 26, if used, from the database 17 based on the types ofcomparisons being performed. First, if the comparisons are selfreferencing with respect to the measures stored in the individualpatient care record 23 (block 152), the reference device and derivedmeasures set 24 a and reference quality of life and symptom measures set25 a, if used, are retrieved for the individual patient from thedatabase 17 (block 153). Next, if the comparisons are peer groupreferencing with respect to measures stored in the patient care records23 for a health disorder- or disease-specific peer group (block 154),the reference device and derived measures set 24 a and reference qualityof life and symptom measures set 25 a, if used, are retrieved from eachpatient care record 23 for the peer group from the database 17 (block155). Data for each measure (e.g., minimum, maximum, averaged, standarddeviation (SD), and trending data) from the reference baseline 26 forthe peer group is then calculated (block 156). Finally, if thecomparisons are population referencing with respect to measures storedin the patient care records 23 for the overall patient population (block157), the reference device and derived measures set 24 a and referencequality of life and symptom measures set 25 a, if used, are retrievedfrom each patient care record 23 from the database 17 (block 158).Minimum, maximum, averaged, standard deviation, and trending data andother numerical processes using the data, as is known in the art, foreach measure from the reference baseline 26 for the peer group is thencalculated (block 159). The routine then returns.

[0056]FIG. 10 is a flow diagram showing the routine for retrievingmonitoring sets 138 for use in the method of FIGS. 8A-8B. The purpose ofthis routine is to retrieve the appropriate monitoring sets 27 from thedatabase 17 based on the types of comparisons being performed. First, ifthe comparisons are self referencing with respect to the measures storedin the individual patient care record 23 (block 160), the device andderived measures set 24 b and quality of life and symptom measures set25 b, if used, are retrieved for the individual patient from thedatabase 17 (block 161). Next, if the comparisons are peer groupreferencing with respect to measures stored in the patient care records23 for a health disorder- or disease-specific peer group (block 162),the device and derived measures set 24 b and quality of life and symptommeasures set 25 b, if used, are retrieved from each patient care record23 for the peer group from the database 17 (block 163). Data for eachmeasure (e.g., minimum, maximum, averaged, standard deviation, andtrending data) from the monitoring sets 27 for the peer group is thencalculated (block 164). Finally, if the comparisons are populationreferencing with respect to measures stored in the patient care records23 for the overall patient population (block 165), the device andderived measures set 24 b and quality of life and symptom measures set25 b, if used, are retrieved from each patient care record 23 from thedatabase 17 (block 166). Minimum, maximum, averaged, standard deviation,and trending data and other numerical processes using the data, as isknown in the art, for each measure from the monitoring sets 27 for thepeer group is then calculated (block 167). The routine then returns.

[0057] FIGS. 11A-11F are flow diagrams showing the routine for testingthreshold limits 139 for use in the method of FIG. 8A and 8B. Thepurpose of this routine is to analyze, compare, and log any differencesbetween the observed, objective measures stored in the referencebaseline 26, if used, and the monitoring sets 27 to the indicatorthresholds 129. Briefly, the routine consists of tests pertaining toeach of the indicators relevant to diagnosing and monitoring respiratoryinsufficiency. The threshold tests focus primarily on: (1) changes toand rates of change for the indicators themselves, as stored in thecombined device and derived measures set 95 (shown in FIG. 4) or similardata structure; and (2) violations of absolute threshold limits whichtrigger an alert. The timing and degree of change may vary with eachmeasure and with the natural fluctuations noted in that measure duringthe reference baseline period. In addition, the timing and degree ofchange might also vary with the individual and the natural history of ameasure for that patient.

[0058] One suitable approach to performing the threshold tests uses astandard statistical linear regression technique using a least squareserror fit. The least squares error fit can be calculated as follows:

y=β ₀+β₁ x  (1)

[0059] $\begin{matrix}{\beta = \frac{{SS}_{xy}}{{SS}_{xx}}} & (2) \\{{SS}_{xy} = {{\sum\limits_{i = 1}^{n}\quad {x_{i}y_{i}}} - \frac{\left( {\sum\limits_{i = 1}^{n}\quad x_{i}} \right)\left( {\sum\limits_{i = 1}^{n}\quad y_{i}} \right)}{n}}} & (3) \\{{SS}_{xx} = {{\sum\limits_{i = 1}^{n}\quad x_{1}^{2}} - \frac{\left( {\sum\limits_{i = 1}^{n}\quad x_{i}} \right)^{2}}{n}}} & (4)\end{matrix}$

[0060] where n is the total number of measures, x_(i) is the time of dayfor measure i, and y_(i) is the value of measure i, β₁ is the slope, andβ₀ is the y-intercept of the least squares error line. A positive slopeβ₁ indicates an increasing trend, a negative slope β₁ indicates adecreasing trend, and no slope indicates no change in patient conditionfor that particular measure. A predicted measure value can be calculatedand compared to the appropriate indicator threshold 129 for determiningwhether the particular measure has either exceeded an acceptablethreshold rate of change or the absolute threshold limit.

[0061] For any given patient, three basic types of comparisons betweenindividual measures stored in the monitoring sets 27 are possible: selfreferencing, peer group, and general population, as explained above withreference to FIG. 6. In addition, each of these comparisons can includecomparisons to individual measures stored in the pertinent referencebaselines 24.

[0062] The indicator thresholds 129 for detecting a trend indicatingprogression into a state of respiratory insufficiency or a state ofimminent or likely respiratory insufficiency, for example, over a oneweek time period, can be as follows:

[0063] (1) Respiratory rate (block 170): If the respiratory rate hasincreased over 1.0 SD from the mean respiratory rate in the referencebaseline 26 (block 171), the increased respiratory rate and time spanover which it occurs are logged in the combined measures set 95 (block172).

[0064] (2) Heart rate (block 173): If the heart rate has increased over1.0 SD from the mean heart rate in the reference baseline 26 (block174), the increased heart rate and time span over which it occurs arelogged in the combined measures set 95 (block 175).

[0065] (3) Transthoracic impedance (block 176): If the transthoracicimpedance has increased over 1.0 SD from the mean transthoracicimpedance in the reference baseline 26 (block 177), the increasedtransthoracic impedance and time span are logged in the combinedmeasures set 95 (block 178).

[0066] (4) The ventilatory tidal volume (block 179): If the tidal volumehas increased over 1.0 SD from the tidal volume score in the referencebaseline 26 (block 180), the increased tidal volume score and time spanare logged in the combined measures set 95 (block 181).

[0067] (5) Arterial oxygen score (block 182): If the arterial oxygenscore has decreased over 1.0 SD from the arterial oxygen score in thereference baseline 26 (block 183), the decreased arterial oxygen scoreand time span are logged in the combined measures set 95 (block 184).

[0068] (6) Arterial carbon dioxide score (block 185): If the arterialcarbon dioxide score has decreased over 1.0 SD from the arterial carbondioxide score in the reference baseline 26 (block 186), the decreasedarterial carbon dioxide score and time span are logged in the combinedmeasures set 95 (block 187).

[0069] (7) Patient activity score (block 188): If the mean patientactivity score has decreased over 1.0 SD from the mean patient activityscore in the reference baseline 26 (block 189), the decreased patientactivity score and time span are logged in the combined measures set 95(block 190).

[0070] (8) Temperature (block 191): If the patient temperature score hasincreased over 1.0 SD from the mean patient temperature score in thereference baseline 26 (block 192), the increased patient temperaturescore and the time span are logged in the combined measures set 95(block 193).

[0071] (9) Spikes in patient activity (block 194): If short-lived spikesin the patient activity score occur over time periods under 5 minutescompared to the reference baseline 26 (block 195), the spike in patientactivity score and time span are logged in the combined measures set 95(block 196).

[0072] (10) Spikes in pulmonary arterial pressure (PAP) (block 197): Ifshort-lived spikes in the pulmonary arterial pressure score occur overtime periods under 5 minutes compared to the reference baseline 26(block 198), the spike in the pulmonary arterial pressure score and timespan are logged in the combined measures set 95 (block 199). In thedescribed embodiment, the mean arterial pressure on any spike in thearterial pressure tracing could be utilized.

[0073] (11) Exercise tolerance quality of life (QOL) measures (block200): If the exercise tolerance QOL has decreased over 1.0 SD from themean exercise tolerance in the reference baseline 26 (block 201), thedecrease in exercise tolerance and the time span over which it occursare logged in the combined measures set 95 (block 202).

[0074] (12) Respiratory distress quality of life (QOL) measures (block203): If the respiratory distress QOL measure has deteriorated by morethan 1.0 SD from the mean respiratory distress QOL measure in thereference baseline 26 (block 204), the increase in respiratory distressand the time span over which it occurs are logged in the combinedmeasures set 95 (block 205).

[0075] (13) Spikes in right ventricular (RV) pressure (block 206): Ifshort-lived spikes in the right ventricular pressure occur over timeperiods under 5 minutes compared to the reference baseline 26 (block207), the spike in the right ventricular pressure and time span arelogged in the combined measures set 95 (block 208).

[0076] (14) Spikes in transthoracic impedance (TTZ) (block 209): Ifshort-lived spikes in the transthoracic impedance occur over timeperiods under 5 minutes compared to the reference baseline 26 (block210), the spike in the transthoracic impedance and time span are loggedin the combined measures set 95 (block 211).

[0077] (15) Atrial fibrillation (block 212): The presence or absence ofatrial fibrillation (AF) is determined and, if present (block 213),atrial fibrillation is logged (block 214).

[0078] (16) Rhythm changes (block 215): The type and sequence of rhythmchanges is significant and is determined (block 216) based on the timingof the relevant rhythm measure, such as sinus rhythm. For instance, afinding that a rhythm change to atrial fibrillation precipitatedrespiratory measures changes can indicate therapy directions againstatrial fibrillation rather than the primary development of respiratoryinsufficiency. Thus, if there are rhythm changes (block 217), thesequence of the rhythm changes and time span are logged (block 211).

[0079] Note also that an inversion of the indicator thresholds 129defined above could similarly be used for detecting a trend in diseaseregression. One skilled in the art would recognize that these measureswould vary based on whether or not they were recorded during rest orduring activity and that the measured activity score can be used toindicate the degree of patient rest or activity. The patient activityscore can be determined via an implantable motion detector, for example,as described in U.S. Pat. No. 4,428,378, issued Jan. 31, 1984, toAnderson et al., the disclosure of which is incorporated herein byreference.

[0080] The indicator thresholds 129 for detecting a trend towards astate of respiratory insufficiency can also be used to declare, apriori, respiratory insufficiency present, regardless of pre-existingtrend data when certain limits are established, such as:

[0081] (1) An absolute limit of arterial oxygen (block 182) less than 85mm Hg is an a priori definition of respiratory insufficiency fromdecreased oxygenation.

[0082] (2) An absolute limit of arterial carbon dioxide (block 185)falling below 25 mm Hg (in the absence of marked exercise) or greaterthan 50 mm Hg are both a priori definitions of respiratory insufficiencyas indicated by hyperventilation and hypoventilation, respectively.

[0083]FIG. 12 is a flow diagram showing the routine for evaluating theonset, progression, regression and status quo of respiratoryinsufficiency 140 for use in the method of FIG. 8A and 8B. The purposeof this routine is to evaluate the presence of sufficient indicia towarrant a diagnosis of the onset, progression, regression, and statusquo of respiratory insufficiency. Quality of life and symptom measures25 a, 25 b can be included in the evaluation (block 230) by determiningwhether any of the individual quality of life and symptom measures 25 a,25 b have changed relative to the previously collected quality of lifeand symptom measures from the monitoring sets 27 and the referencebaseline 26, if used. For example, an increase in the shortness ofbreath measure 87 and exercise tolerance measure 92 would corroborate afinding of respiratory insufficiency. Similarly, a transition from NYHAClass II to NYHA Class III would indicate a deterioration or,conversely, a transition from NYHA Class III to NYHA Class II statuswould indicate improvement or progress when adapting the NYHAclassifications for their parallel in lung disorders. Incorporating thequality of life and symptom measures 25 a, 25 b into the evaluation canhelp, in part, to refute or support findings based on physiologicaldata. Next, a determination as to whether any changes to interventivemeasures are appropriate based on threshold stickiness (“hysteresis”) ismade (block 231), as further described below with reference to FIG. 16.

[0084] The routine returns upon either the determination of a finding orelimination of all factors as follows. If a finding of respiratoryinsufficiency was not previously diagnosed (block 232), a determinationof disease onset is made (block 233), as further described below withreference to FIGS. 13A-13C. Otherwise, if respiratory insufficiency waspreviously diagnosed (block 232), a further determination of eitherdisease progression or worsening (block 234) or regression or improving(block 235) is made, as further described below with reference to FIGS.14A-14C and 15A-15C, respectively. If, upon evaluation, neither diseaseonset (block 233), worsening (block 234) or improving (block 235) isindicated, a finding of status quo is appropriate (block 236) and noted(block 237). Otherwise, respiratory insufficiency and the relatedoutcomes are actively managed (block 238) through the administration of,non-exclusively, antibiotic and antiviral therapies, bronchodilatortherapies, oxygen therapies, antiinflammation therapies, electricaltherapies, mechanical therapies, and other therapies as are known in theart. The management of respiratory insufficiency is described, by way ofexample, in A. S. Fauci et al. (Eds.), “Harrison's Principles ofInternal Medicine,” pp. 1407-1491, McGraw-Hill, 14th Ed. (1997), thedisclosure of which is incorporated herein by reference. The routinethen returns.

[0085] FIGS. 13A-13C are flow diagrams showing the routine fordetermining an onset of respiratory insufficiency 232 for use in theroutine of FIG. 12. Respiratory insufficiency is possible based on twogeneral symptom categories: reduced exercise capacity (block 244) andrespiratory distress (block 256). An effort is made to diagnoserespiratory insufficiency manifesting primarily as resulting in reducedexercise capacity (block 244) and/or increased respiratory distress(block 256). Reduced exercise capacity and respiratory distress cangenerally serve as markers of low systemic arterial oxygenation. Theclinical aspects of respiratory insufficiency are described, by way ofexample, in A. S. Fauci et al. (Eds.), “Harrison's Principles ofInternal Medicine,” pp. 1410-1419, McGraw-Hill, 14th Ed. (1997), thedisclosure of which is incorporated herein by reference.

[0086] As primary pulmonary disease considerations, multiple individualindications (blocks 240-243,245-253) should be present for the twoprincipal findings of respiratory insufficiency related reduced exercisecapacity (block 244), or respiratory insufficiency related respiratorydistress (block 256), to be indicated, both for disease onset orprogression. The presence of primary key findings alone can besufficient to indicate an onset of respiratory insufficiency andsecondary key findings serve to corroborate disease onset. Note thepresence of any abnormality can trigger an analysis for the presence orabsence of secondary disease processes, such as the presence of atrialfibrillation or congestive heart failure. Secondary diseaseconsiderations can be evaluated using the same indications (see, e.g.,blocks 141-144 of FIGS. 8A-8B), but with adjusted indicator thresholds129 (shown in FIG. 5) triggered at a change of 0.5 SD, for example,instead of 1.0 SD.

[0087] In the described embodiment, the reduced exercise capacity andrespiratory distress findings (blocks 244, 251) can be established byconsolidating the individual indications (blocks 240-243,245-253) inseveral ways. First, in a preferred embodiment, each individualindication (blocks 240-243,245-253) is assigned a scaled index valuecorrelating with the relative severity of the indication. For example,decreased cardiac output (block 240) could be measured on a scale from‘1’ to ‘5’ wherein a score of ‘1’ indicates no change in cardiac outputfrom the reference point, a score of ‘2’ indicates a change exceeding0.5 SD, a score of ‘3’ indicates a change exceeding 1.0 SD, a score of‘4’ indicates a change exceeding 2.0 SD, and a score of ‘5’ indicates achange exceeding 3.0 SD. The index value for each of the individualindications (blocks 240-243,245-253) can then either be aggregated oraveraged with a result exceeding the aggregate or average maximumindicating an appropriate respiratory insufficiency finding.

[0088] Preferably, all scores are weighted depending upon theassignments made from the measures in the reference baseline 26. Forinstance, arterial partial pressure of oxygen 102 could be weighted moreimportantly than respiratory rate 104 if the respiratory rate in thereference baseline 26 is particularly high at the outset, making thedetection of further disease progression from increases in respiratoryrate, less sensitive. In the described embodiment, arterial partialpressure of oxygen 102 receives the most weight in determining a reducedexercise capacity finding whereas arterial partial pressure of carbondioxide 107 receives the most weight in determining a respiratorydistress or dyspnea finding.

[0089] Alternatively, a simple binary decision tree can be utilizedwherein each of the individual indications (blocks 240-243,245-253) iseither present or is not present. All or a majority of the individualindications (blocks 240-243,245-253) should be present for the relevantrespiratory insufficiency finding to be affirmed. Other forms ofconsolidating the individual indications (blocks 240-243, 245-253) arefeasible.

[0090] FIGS. 14A-14C are flow diagrams showing the routine fordetermining a progression or worsening of respiratory insufficiency 234for use in the routine of FIG. 12. The primary difference between thedeterminations of disease onset, as described with reference to FIGS.13A-13C, and disease progression is the evaluation of changes indicatedin the same factors present in a disease onset finding. Thus, a revisedrespiratory insufficiency finding is possible based on the same twogeneral symptom categories: reduced exercise capacity (block 274) andrespiratory distress (block 286). The same factors which need beindicated to warrant a diagnosis of respiratory insufficiency onset areevaluated to determine disease progression.

[0091] Similarly, FIGS. 15A-15C are flow diagrams showing the routinefor determining a regression or improving of respiratory distress 235for use in the routine of FIG. 12. The same factors as described abovewith reference to FIGS. 13A-13C and 14A-14C, trending in oppositedirections from disease onset or progression, are evaluated to determinedisease regression. As primary cardiac disease considerations, multipleindividual indications (blocks 300-303, 305-313) should be present forthe two principal findings of respiratory insufficiency related reducedexercise capacity (block 304), or respiratory insufficiency relatedrespiratory distress (block 316), to indicate disease regression.

[0092]FIG. 16 is a flow diagram showing the routine for determiningthreshold stickiness (“hysteresis”) 231 for use in the method of FIG.12. Stickiness, also known as hysteresis, is a medical practice doctrinewhereby a diagnosis or therapy will not be changed based upon small ortemporary changes in a patient reading, even though those changes mighttemporarily move into a new zone of concern. For example, if a patientmeasure can vary along a scale of ‘1’ to ‘10’ with ‘10’ being worse, atransient reading of ‘6,’ standing alone, on a patient who hasconsistently indicated a reading of ‘5’ for weeks will not warrant achange in diagnosis without a definitive prolonged deterioration firstbeing indicated. Stickiness dictates that small or temporary changesrequire more diagnostic certainty, as confirmed by the persistence ofthe changes, than large changes would require for any of the monitored(device) measures. Stickiness also makes reversal of importantdiagnostic decisions, particularly those regarding life-threateningdisorders, more difficult than reversal of diagnoses of modest import.As an example, automatic external defibrillators (AEDs) manufactured byHeartstream, a subsidiary of Agilent Technologies, Seattle, Wash.,monitor heart rhythms and provide interventive shock treatment for thediagnosis of ventricular fibrillation. Once diagnosis of ventricularfibrillation and a decision to shock the patient has been made, apattern of no ventricular fibrillation must be indicated for arelatively prolonged period before the AED changes to a “no-shock”decision. As implemented in this AED example, stickiness mandatescertainty before a decision to shock is disregarded. In practice,stickiness also dictates that acute deteriorations in disease state aretreated aggressively while chronic, more slowly progressing diseasestates are treated in a more tempered fashion. However, in the case ofsome of the parameters being followed, such as activity and pulmonaryartery systolic pressure, abrupt spikes in these measures can beindicative of coughing and therefore helpful in indicating the onset ofa disorder that might lead to pulmonary insufficiency.

[0093] Thus, if the patient status indicates a status quo (block 330),no changes in treatment or diagnosis are indicated and the routinereturns. Otherwise, if the patient status indicates a change away fromstatus quo (block 330), the relative quantum of change and the length oftime over which the change has occurred is determinative. If the changeof approximately 0.5 SD has occurred over the course of about one month(block 331), a gradually deteriorating condition exists (block 332) anda very tempered diagnostic, and if appropriate, treatment program isundertaken. If the change of approximately 1.0 SD has occurred over thecourse of about one week (block 333), a more rapidly deterioratingcondition exists (block 334) and a slightly more aggressive diagnostic,and if appropriate, treatment program is undertaken. If the change ofapproximately 2.0 SD has occurred over the course of about one day(block 335), an urgently deteriorating condition exists (block 336) anda moderately aggressive diagnostic, and if appropriate, treatmentprogram is undertaken. If the change of approximately 3.0 SD hasoccurred over the course of about one hour (block 337), an emergencycondition exists (block 338) and an immediate diagnostic, and ifappropriate, treatment program is undertaken as is practical. Finally,if the change and duration fall outside the aforementioned ranges(blocks 331-338), an exceptional condition exists (block 339) and thechanges are reviewed manually, if necessary. The routine then returns.These threshold limits and time ranges may then be adapted dependingupon patient history and peer-group guidelines.

[0094] The present invention provides several benefits. One benefit isimproved predictive accuracy from the outset of patient care when areference baseline is incorporated into the automated diagnosis. Anotherbenefit is an expanded knowledge base created by expanding themethodologies applied to a single patient to include patient peer groupsand the overall patient population. Collaterally, the informationmaintained in the database could also be utilized for the development offurther predictive techniques and for medical research purposes. Yet afurther benefit is the ability to hone and improve the predictivetechniques employed through a continual reassessment of patient therapyoutcomes and morbidity rates.

[0095] Other benefits include an automated, expert system approach tothe cross-referral, consideration, and potential finding or eliminationof other diseases and health disorders with similar or relatedetiological indicators and for those other disorders that may have animpact on respiratory insufficiency. Although disease specific markerswill prove very useful in discriminating the underlying cause ofsymptoms, many diseases, other than respiratory insufficiency, willalter some of the same physiological measures indicative of respiratoryinsufficiency. Consequently, an important aspect of considering thepotential impact of other disorders will be, not only the monitoring ofdisease specific markers, but the sequencing of change and the temporalevolution of more general physiological measures, for examplerespiratory rate, pulmonary artery diastolic pressure, and cardiacoutput, to reflect disease onset, progression or regression in more thanone type of disease process, especially congestive heart failure fromwhatever cause.

[0096] While the invention has been particularly shown and described asreferenced to the embodiments thereof, those skilled in the art willunderstand that the foregoing and other changes in form and detail maybe made therein without departing from the spirit and scope of theinvention.

What is claimed is:
 1. A system for providing diagnosis and monitoringof respiratory insufficiency for use in automated patient care,comprising: a comparison module comparing at least one recordedphysiological measure to at least one other recorded physiologicalmeasure on a substantially regular basis to quantify a change in patientpathophysiological status for equivalent patient information; and ananalysis module evaluating an absence, an onset, a progression, aregression, and a status quo of respiratory insufficiency dependent uponthe change in patient pathophysiological status.
 2. A system accordingto claim 1, further comprising: a diagnostic module comparing the changein patient pathophysiological status to an indicator thresholdcorresponding to a quantifiable physiological measure indicative ofrespiratory insufficiency.
 3. A system according to claim 1, furthercomprising: a management module managing the respiratory insufficiencythrough administration of at least one of antibiotic and antiviraltherapies, bronchodilator therapies, oxygen therapies, antiinflammationtherapies, electrical therapies, and mechanical therapies.
 4. A systemaccording to claim 1, further comprising: a database module retrievingthe at least one recorded physiological measure and the at least oneother recorded physiological measure from monitoring sets stored in adatabase.
 5. A system according to claim 4, further comprising: a serversystem collecting the at least one recorded physiological measure andthe at least one other recorded physiological measure into eachmonitoring set recorded on a substantially continuous basis or derivedtherefrom.
 6. A system according to claim 5, further comprising: atleast one of an implantable medical device and an external medicaldevice recording the at least one recorded physiological measure and theat least one other recorded physiological measure.
 7. A system accordingto claim 1, further comprising: the analysis module evaluating anabsence, an onset, a progression, a regression, and a status quo ofdiseases other than respiratory insufficiency dependent upon the changein patient pathophysiological status.
 8. A system according to claim 1,further comprising: a diagnostic module comparing at least one recordedquality of life measure to at least one other recorded quality of lifemeasure on a substantially regular basis to qualify a change in patientpathophysiological status.
 9. A system according to claim 1, furthercomprising: a stored stickiness indicator defined for at least onephysiological measure corresponding to a temporal boundary on one ofpatient diagnosis and treatment; a diagnostic module timing each changein patient pathophysiological status for the equivalent patientinformation and determining one of a revised patient diagnosis andtreatment responsive to each change in patient pathophysiological statuswith a timing exceeding the stickiness indicator.
 10. A system accordingto claim 1, further comprising: a diagnostic module comparing the changein patient pathophysiological status to a reference baseline comprisingrecorded physiological measures recorded during an initial time period.11. A system according to claim 1, further comprising: a diagnosticmodule comparing the change in patient pathophysiological status toequivalent patient information from at least one of an individualpatient, a peer group, and a overall patient population.
 12. A systemaccording to claim 1, further comprising: a diagnostic module gradingthe change in patient pathophysiological status on a fixed scale basedon a degree of deviation from a pre-defined indicator threshold andperforming a summation over a plurality of the graded changes todetermine an overall change in patient pathophysiological status.
 13. Asystem according to claim 1, further comprising: a diagnostic moduledetermining probabilistic weightings of the change in patientpathophysiological status on a statistical deviation and trends vialinear fits from a pre-defined indicator threshold and performing asummation over a plurality of the probabilistic weightings to determinean overall change in patient pathophysiological status.
 14. A method forproviding diagnosis and monitoring of respiratory insufficiency for usein automated patient care, comprising: comparing at least one recordedphysiological measure to at least one other recorded physiologicalmeasure on a substantially regular basis to quantify a change in patientpathophysiological status for equivalent patient information; andevaluating an absence, an onset, a progression, a regression, and astatus quo of respiratory insufficiency dependent upon the change inpatient pathophysiological status.
 15. A method according to claim 14,further comprising: comparing the change in patient pathophysiologicalstatus to an indicator threshold corresponding to a quantifiablephysiological measure indicative of respiratory insufficiency.
 16. Amethod according to claim 14, further comprising: managing therespiratory insufficiency through administration of at least one ofantibiotic and antiviral therapies, bronchodilator therapies, oxygentherapies, antiinflammation therapies, electrical therapies, andmechanical therapies.
 17. A method according to claim 14, furthercomprising: retrieving the at least one recorded physiological measureand the at least one other recorded physiological measure frommonitoring sets stored in a database.
 18. A method according to claim17, further comprising: collecting the at least one recordedphysiological measure and the at least one other recorded physiologicalmeasure into each monitoring set recorded on a substantially continuousbasis or derived therefrom.
 19. A method according to claim 18, furthercomprising: recording the at least one recorded physiological measureand the at least one other recorded physiological measure with at leastone of an implantable medical device and an external medical device. 20.A method according to claim 14, further comprising: evaluating anabsence, an onset, a progression, a regression, and a status quo ofdiseases other than respiratory insufficiency dependent upon the changein patient pathophysiological status.
 21. A method according to claim14, further comprising: comparing at least one recorded quality of lifemeasure to at least one other recorded quality of life measure on asubstantially regular basis to qualify a change in patientpathophysiological status.
 22. A method according to claim 14, furthercomprising: defining a stickiness indicator for at least onephysiological measure corresponding to a temporal boundary on one ofpatient diagnosis and treatment; timing each change in patientpathophysiological status for the equivalent patient information; anddetermining one of a revised patient diagnosis and treatment responsiveto each change in patient pathophysiological status with a timingexceeding the stickiness indicator.
 23. A method according to claim 14,further comprising: comparing the change in patient pathophysiologicalstatus to a reference baseline comprising recorded physiologicalmeasures recorded during an initial time period.
 24. A method accordingto claim 14, further comprising: comparing the change in patientpathophysiological status to equivalent patient information from atleast one of an individual patient, a peer group, and a overall patientpopulation.
 25. A method according to claim 14, further comprising:grading the change in patient pathophysiological status on a fixed scalebased on a degree of deviation from a pre-defined indicator threshold;and performing a summation over a plurality of the graded changes todetermine an overall change in patient pathophysiological status.
 26. Amethod according to claim 14, further comprising: determiningprobabilistic weightings of the change in patient pathophysiologicalstatus on a statistical deviation and trends via linear fits from apre-defined indicator threshold; and performing a summation over aplurality of the probabilistic weightings to determine an overall changein patient pathophysiological status.
 27. A computer-readable storagemedium for a device holding code for performing the method according toclaim
 14. 28. A system for analyzing a patient status for respiratoryinsufficiency for use in automated patient care, comprising: a serversystem receiving a set of one or more physiological measures relating topatient information recorded on a substantially continuous basis orderived therefrom; a database module storing the physiological measuresset into a patient care record for an individual patient into adatabase; and an analyzer analyzing one or more of the physiologicalmeasures in the physiological measures set relative to one or more otherphysiological measures to determine a pathophysiology indicating anabsence, an onset, a progression, a regression, and a status quo ofrespiratory insufficiency.
 29. A system according to claim 28, furthercomprising: the analyzer analyzing the physiological measures in thephysiological measures set relative to the other physiological measuresto determine a pathophysiology indicating an absence, an onset, aprogression, a regression, and a status quo of diseases other thanrespiratory insufficiency.
 30. A system according to claim 28, furthercomprising: the server system receiving a set of one or more quality oflife measures relating to patient information recorded on asubstantially continuous basis or derived therefrom; the database modulestoring the quality of life measures set into the patient care recordfor the individual patient into the database; and the analyzer analyzingthe quality of life measures in the physiological measures set relativeto the other quality of life measures to determine a pathophysiologyindicating an absence, an onset, a progression, a regression, and astatus quo of respiratory insufficiency.
 31. A system according to claim28, further comprising: the server system receiving a set of one or morebaseline physiological measures relating to patient information recordedduring an initial time period or derived therefrom; the database modulestoring the baseline physiological measures set into the patient carerecord for the individual patient into the database; and the analyzeranalyzing the physiological measures in the physiological measures setrelative to the baseline physiological measures to determine apathophysiology indicating an absence, an onset, a progression, aregression, and a status quo of respiratory insufficiency.
 32. A systemaccording to claim 28, further comprising: a comparison moduleretrieving the other physiological measures from measures sets for atleast one of an individual patient, a peer group, and a overall patientpopulation.
 33. A method for analyzing a patient status for respiratoryinsufficiency for use in automated patient care, comprising: receiving aset of one or more physiological measures relating to patientinformation recorded on a substantially continuous basis or derivedtherefrom; storing the physiological measures set into a patient carerecord for an individual patient into a database; and analyzing one ormore of the physiological measures in the physiological measures setrelative to one or more other physiological measures to determine apathophysiology indicating an absence, an onset, a progression, aregression, and a status quo of respiratory insufficiency.
 34. A methodaccording to claim 33, further comprising: analyzing the physiologicalmeasures in the physiological measures set relative to the otherphysiological measures to determine a pathophysiology indicating anabsence, an onset, a progression, a regression, and a status quo ofdiseases other than respiratory insufficiency.
 35. A method according toclaim 33, further comprising: receiving a set of one or more quality oflife measures relating to patient information recorded on asubstantially continuous basis or derived therefrom; storing the qualityof life measures set into the patient care record for the individualpatient into the database; and analyzing the quality of life measures inthe physiological measures set relative to the other quality of lifemeasures to determine a pathophysiology indicating an absence, an onset,a progression, a regression, and a status quo of respiratoryinsufficiency.
 36. A method according to claim 33, further comprising:receiving a set of one or more baseline physiological measures relatingto patient information recorded during an initial time period or derivedtherefrom; storing the baseline physiological measures set into thepatient care record for the individual patient into the database; andanalyzing the physiological measures in the physiological measures setrelative to the baseline physiological measures to determine apathophysiology indicating an absence, an onset, a progression, aregression, and a status quo of respiratory insufficiency.
 37. A methodaccording to claim 33, further comprising: retrieving the otherphysiological measures from measures sets for at least one of anindividual patient, a peer group, and a overall patient population. 38.A computer-readable storage medium for a device holding code forperforming the method according to claim 33.